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Extracting 3D Parametric Curves from 2D Images of Helical Objects

Lookup NU author(s): Dr Chris Willcocks, Phillip Jackson, Professor Boguslaw ObaraORCiD

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2017.

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Abstract

© 2017 IEEE. Helical objects occur in medicine, biology, cosmetics, nanotechnology, and engineering. Extracting a 3D parametric curve from a 2D image of a helical object has many practical applications, in particular being able to extract metrics such as tortuosity, frequency, and pitch. We present a method that is able to straighten the image object and derive a robust 3D helical curve from peaks in the object boundary. The algorithm has a small number of stable parameters that require little tuning, and the curve is validated against both synthetic and real-world data. The results show that the extracted 3D curve comes within close Hausdorff distance to the ground truth, and has near identical tortuosity for helical objects with a circular profile. Parameter insensitivity and robustness against high levels of image noise are demonstrated thoroughly and quantitatively.

Publication metadata

Author(s): Willcocks CG, Jackson PTG, Nelson CJ, Obara B

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Pattern Analysis and Machine Intelligence

Year: 2017

Volume: 39

Issue: 9

Pages: 1757-1769

Print publication date: 01/09/2017

Online publication date: 26/09/2016

Acceptance date: 20/09/2016

Date deposited: 04/05/2021

ISSN (print): 0162-8828

ISSN (electronic): 1939-3539

Publisher: IEEE

URL: https://doi.org/10.1109/TPAMI.2016.2613866

DOI: 10.1109/TPAMI.2016.2613866

PubMed id: 28114058

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